Use Of Multiple Linear Regression Research Articles

MACHINE LEARNING BASED STUDENT ACHIEVEMENT PERFORMANCE PREDICTION WEB APPLICATION

The use of multiple linear regression in our research is critical for determining the factors that have a greater impact on student performance index. Machine learning studies that employ multiple linear regression models to forecast student performance index aim to increase educational processes and individual student ability. These studies search to gain a deeper understanding of the factors that impact academic success by examining various variables that affect student performance. In the literature has demonstrated that such models achieve high levels of accuracy and can reliably predict student performance. In our study, we constructed and trained a multiple linear regression model. The dataset was divided into training and test sets, and the model was assessed using these datasets. Performance of the model was evaluated using various metrics such as MAE, MSE, R2, RMSE, and Accuracy(ACC). The results obtained indicated that the model performed exceptionally well, indicating its ability to make precise predictions. Especially, the coefficient of determination (R2) was 0.99, and the ACC value was 0.994, underscoring the model's exceptional ability to accurately explain the data. The focus of our research is to assess the precision and dependability of the findings derived from analyzing the impact of different independent factors on student achievement, utilizing a multiple linear regression model. Moreover, we have created a web interface using the Flask web module that enables the prediction of student performance based on inputting new variables.

Non-standard Working Practices and Nurses’ Job Performance: Experience from Public Hospitals in Tanzania

Non-standard working practices are common among nurses because they spend more time directly caring for patients than other healthcare professionals. Thus, nurses’ performance is impacted by how work is organized, whereas various component of the working practice may lead to a decrease in performance. The purpose of this study was to examine the influence of non-standard working practices on performance of nurses in public hospitals in Tanzania. Specifically, the study focused on assessing the influence of schedule variation, schedule uncertainty, and schedule intensity on performance of nurses in Tanzanian public hospitals. The study adopted descriptive survey research design using qualitative and quantitative methods. Stratified random sampling technique was used, whereas a sample size of 381 nurses was randomly chosen from a target population of 404. Quantitative data were analyzed through thematic analysis technique whereas qualitative data were analyzed by the use of multiple linear regression, one-way Analysis of Variance, independent t-test, and Pearson correlation. Qualitative data were entered, coded and analyzed using summative content analysis and presented in percentages, in tables, figures and charts. Correlation analysis was used to test the direction of relationship between the independent variables and dependent variable. Multiple regressions were used to test whether schedule variation, schedule uncertainty, and schedule intensity had any influence on nurses’ performance. The study established that schedule variation, schedule uncertainty, and schedule intensity contributed negatively to nurses’ performance. Furthermore, the study approved all three study hypothesis as it was found that all schedule variation, schedule uncertainty and schedule intensity had significant statistical influence on job performance of nurses. The study concluded that exposure to non-standard working practices were common among nurses in public hospitals in Tanzania. Further, the study concluded that nurses in the study area were more affected by schedule variation and schedule uncertainty. The study recommended the adoption of strategies aimed at minimizing the effects non-standard schedules to promote nurses’ performance.

Prenatal exposure to pyrethroids and chlorpyrifos and IQ in 7-year-old children from the Odense Child Cohort

BackgroundOrganophosphates and pyrethroids are two major groups of insecticides used for crop protection worldwide. They are neurotoxicants and exposure during vulnerable windows of brain development may have long-term impact on human neurodevelopment. Only few longitudinal studies have investigated associations between prenatal exposure to these substances and intelligence quotient (IQ) at school age in populations with low, mainly dietary, exposure. ObjectiveTo investigate associations between maternal urinary concentrations of insecticide metabolites at gestational week 28 and IQ in offspring at 7-years of age. Materials and methodsData was derived from the Odense Child Cohort (OCC). Metabolites of chlorpyrifos (TCPy) and pyrethroids (3-PBA, cis- and trans-DCCA, 4-F-3PBA, cis-DBCA) were measured in maternal urine collected at gestational week (GW) 28. An abbreviated version of the Danish Wechsler Intelligence Scale for Children fifth edition (WISC-V) consisting of four subtests to estimate full scale IQ (FSIQ) was administered by trained psychologists. Data were analyzed by use of multiple linear regression and adjusted for confounders. Results812 mother/child-pairs were included. Median concentrations were 0.21 μg/L for 3-PBA, 1.67 μg/L for TCPy and the mean IQ for children were 99.4. Null association between maternal 3-PBA and child IQ at 7 years was seen, but with trends suggesting an inverse association. There was a significant association for maternal TCPy and child IQ at mid-level exposure. Trans-DCCA above the level of detection (LOD) was also associated with slightly lower child IQ, but the association was also not statistically significant. ConclusionsWe found no significant associations between maternal 3-PBA metabolites and child IQ at 7 years, but with trends suggesting an inverse association. A non-significant trend between maternal TCPy exposure and child IQ in 7-year-children was seen even in this low exposed population. Given the widespread exposure and increasing use of insecticides, this should be elaborated in future studies.

Determine The Factors That Influence Profit Growth at Village Credit Institutions in Badung District

Village Credit Institutions or VCIs, function similarly to banks. There are variations in performance measurement, though (health status). The VCIs employ the Bali Province Governor's operational regulations number. 44 of 2017, which accounts for this discrepancy. The VCIs of Bali Province's Badung Regency were one of the VCIs examined. Because Badung Regency VCIs have the highest assets when compared to other VCIs in Bali Province's districts and cities, the goal is to look at their profit growth. The likelihood that VCIs may fail owing to poor management and a lack of public transparency on their state of health does not disappear even with their substantial asset base. This study examines the effect of VCI health on profit growth empirically. The Capital, Asset, Management, Earnings, and Liquidity (CAMEL) examination on profit growth is part of this degree of soundness. Study observation period: 2016–2022. Using a quantitative research approach and secondary data from Empowerment Institutions of Village Credit Institutions (EIVCI) in the Badung Regency, the total population is 854 VCIs. A non-probability sampling strategy combined with a purposive sampling technique was used to determine the sample. This study's analysis method makes use of multiple linear regression. The study's findings indicate that operational expenses have a beneficial impact on profit growth when it comes to operating income, loan-to-deposit ratio, net profit margin, and return on assets. In the meantime, profit growth is negatively impacted by the caliber of producing assets. In the meanwhile, profit growth is unaffected by the capital adequacy ratio and current ratio. The research's practical application is to arm VCIs with knowledge so they can train VCI administrators to manage VCIs. In addition, the availability of profit growth analysis using CAMEL helps boost public trust in the use of VCIs.

The Dynamics of School Environment and the Impact of Parental Attention on the Academic Achievement of Students at SMA Negeri 1 Ketapang Sampang

In the current era of globalization, students are faced with demands to consider the factors that influence the achievement of their learning outcomes. This research aims to reveal the role of an effective school environment and parental attention in improving the quality of student education. Quantitative methods were used in this research. In this research, this approach was chosen to provide a more measurable understanding of the role of the school environment and parental attention on student learning outcomes. The research population includes all students of SMA Negeri 1 Ketapang Sampang so that the research results can be more generally applied to this group. The use of multiple linear regression as a data analysis tool allows researchers to understand the extent to which certain variables, such as school environment and parental attention, contribute to variability in student learning outcomes. The findings show that the school environment has a fairly large role in determining student learning outcomes. Apart from that, emotional involvement and parental support also play a role in developing student academic achievement. With a school environment that supports learning and positive contributions from parents, it is hoped that student learning outcomes will improve.

Analysis of factors influencing the amount of rice production in Banggai Regency

The aim of this research is to find out how land area, workforce and capital influence rice production in Banggai Regency. Purposive sampling, which refers to the data source. This research uses a sampling method that considers certain factors. Examples were taken of up to forty participants. The type of information used in this research is primary data. Interviews with forty respondents produced direct primary data. Procedure Data analysis involved the use of multiple linear regression. The research results show that the volume of rice production in Banggai Regency is greatly influenced and profitable by land area. The impact of labor on output levels is small but positive for rice in Banggai Regency. The capital impact is negligible and detrimental to the level of rice production in Banggai Regency. The amount of rice production in Banggai Regency is greatly influenced by land area, labor and capital.

Determinan Pengungkap Akuntansi Sumber Daya Manusia dalam Perusahaan Manufaktur di Bursaf Efek Indonesiah

The purpose of this study is to ascertain how firm size, profitability, and institutional ownership relate to accounting disclosures related to human resources. The reason this variable was selected is that it is one of the factors that affects disclosure of human resources. This study tests hypotheses using quantitative research methods. The business data used in this study's secondary data comes from the 2022 annual reports of manufacturing firms in the basic industrial and chemical sectors that are listed on the Indonesia Stock Exchange The author employed a purposive sample methodology, which is a sampling strategy that is based on predetermined criteria. Thirty data study samples were employed, all of which were selected according to predefined criteria. This study's data analysis method makes use of multiple linear regression. The study's findings demonstrate that human resource accounting disclosures are positively impacted by a company's size. Disclosures in human resource accounting are influenced by profitability. Accounting disclosures pertaining to human resources benefit from institutional ownership.

Penerapan Internet Banking Dan Mobile Banking Pada Kinerja Keuangan Bank Umum Konvensional Di Indonesia

This study aims to determine the effect of internet banking and mobile banking on the financial performance of conventional commercial banks on the IDX in 2018-2021. This research uses descriptive verification method. The use of multiple linear regression in data processing. Data was collected through non-participant observation methods by taking data in the form of financial reports on the IDX. Patially, only mobile banking has a significant effect on banking financial performance. Simultaneously, the two independent variables have a significant influence on banking financial performance

Estimation of Average Annual Precipitation Considering Spatio-Temporal Variability Through the Use of Multiple Linear Regression in the State of Paraíba

Objective: The objective of this work was to calibrate a multiple linear regression equation to relate average annual precipitations to spatial localization of 145 stations placed on three mesoregions of Paraíba State, Brazil, Borborema, Agreste, and Mata Zone. Theoretical framework: The variability of precipitations in space and time is directly related to the hydrological cycle, land use and occupation, and local economy. In this sense, regional models for rainfall may provide relevant information, supporting the decision-making process associated with water resources planning and management. Method: For the study case, stations with rainfall series with more than 10 years of daily data were considered. The posts that their few flaws in their series had the data filled in by the Inverse Distance Weighted (IDW) deterministic method, which assings weitghted weights to the sample data, Where the influence of one station over another decreases with the distance to the station to be estimated. Results and conclusion: The results were moderate, with a determination coefficient of 0.62 for the studied mesoregions. The average percentage errors of the estimate showed that most of the analyzed surface is classified with errors between 8.3% and 22,8%. Implications of the research: The multiple regression statistical tool was used for precipitation data with high variability, proving effective modeling. This type of study helps with decision-making in water resource management and climate monitoring studies. Originality/value: Using the multiple regression statistical tool, it was possible to obtain a linear equation to estimate the average annual precipitation in three mesoregions of the Paraíba State.

FINANCIAL SHENANIGANS AND COMPANY CHARACTERISTICS IN INDONESIA PUBLIC COMPANY

Financial shenanigans are moves made by a company's management with the intention of falsifying the company's financial status and stated performance. The goal of this study is to clarify if financial considerations and company size have an impact on a company's propensity to engage in financial misdeeds in Indonesian public companies. Financial leverage, financial stability, personal financial need, and return on assets are the financial characteristics that were examined in this study. An Indonesian firm that has gone public serves as the study's sample. Purposive sampling was used in this study's sampling. This study's analysis method makes use of multiple linear regression. The findings revealed that the company was unaffected by all variables except for financial leverage.

Early Fast Cost Estimates of Sewerage Projects Construction Costs Based on Ensembles of Neural Networks

This paper presents research results on the development of an original cost prediction model for construction costs in sewerage projects. The focus is placed on fast cost estimates applicable in the early stages of a project, based on fundamental information available during the initial design phase of sanitary sewers prior to the detailed design. The originality and novelty of this research lie in the application of artificial neural network ensembles, which include a combination of several individual neural networks and the use of simple averaging and generalized averaging approaches. The research resulted in the development of two ensemble-based models, including five neural networks that were trained and tested using data collected from 125 sewerage projects completed in the Czech Republic between 2018 and 2022. The data included information relevant to various aspects of projects and contract costs, updated to account for changes in costs over time. The developed models present satisfactory predictive performance, especially the ensemble model based on simple averaging, which offers prediction accuracy within the range of ±30% (in terms of percentage errors) for over 90% of the training and testing samples. The developed models, based on the ensembles of neural networks, outperformed the benchmark model based on the classical approach and the use of multiple linear regression.

Analysis the impact of bank credit on growth in the industrial sector in Iraq for the period 2008-2021

This research aims to shed light on the reality of industrial production in Iraq for the period 2008-2021 and on the reality, structure of bank credit granted to the industrial sector (both private and government) in the same period. And then measuring and analyzing the effect of this credit in its two parts on the growth of industrial production for the same period. The research adopted the inductive analytical approach in analyzing the available data on bank credit granted to the industrial sector and the added value of industrial production in Iraq, and the econometric approach through the use of multiple linear regression to measure the impact of bank credit on industrial production in Iraq. The research has come to the conclusion that the percentages of bank credit granted to the Iraqi industrial sector suffer from fluctuation, imbalance, and scarcity, and that this credit does not affect the industrial production process, as it was found to be insignificant. When comparing the credit granted by private banks to the credit granted by government banks, it turns out that there is a defect in the credit structure. As the largest proportions of credit granted to industrialists come from government banks, despite their small numbers, and the lowest percentage comes from private banks, despite their large numbers. It was found that the credit of government banks is not significant and does not affect the industrial production process, while the credit of private banks, despite its scarcity, is significant and influential in the industrial production process

A New Approach for Ocean Surface Wind Speed Retrieval Using Sentinel-1 Dual-Polarized Imagery

A synthetic aperture radar (SAR) has the capability to observe ocean surface winds with a high spatial resolution, even under extreme conditions. The purpose of this work was to develop a new method for wind speed retrieval with the combination of SAR dual-polarized signals. In this study, we collected 28 tropical cyclone imageries observed using the Sentinel-1 dual-polarization mode. These imageries were collocated with radiometer wind speed measurements and reanalysis of wind vector products. In the new method, the wind speed was set as the output. VV-polarized (vertical transmitting–vertical receiving polarized) normalized radar cross section (NRCS), incident angle, VH-polarized (vertical transmitting–horizontal receiving polarized) NRCS, and wind direction were set as the inputs. Based on different output combinations, wind retrieval models were developed with multiple linear regression (MLR). According to the validation and comparison, the proposed models performed better than the traditional piecewise VH-polarization geophysical model functions (GMFs). The impact of thermal noise on the retrieval of low wind speeds (<10 m/s) could be partially reduced. The input of wind direction is unnecessary if the combination of VV- and VH-polarized imageries has been utilized. These results suggest that the use of MLR and the dual-polarization combination can improve SAR wind retrieval accuracy. Compared with SMAP measurements, our SAR retrievals can provide fine structures of TC wind fields.

COMPARISON OF LASSO AND ADAPTIVE LASSO METHODS IN IDENTIFYING VARIABLES AFFECTING POPULATION EXPENDITURE

Abstract: Since 2019, the difference in the increase in per capita expenditure of the population has continued to decline, and the most significant was only IDR 18,464 in 2021, indicating that the level of consumption of the population has not improved significantly, and the turnover of the community's economy is also not good. Multiple linear regression is more appropriate than other types of linear regression because it considers the influence of more than one independent variable on the dependent variable. However, problems may arise in the use of multiple linear regression, such as multicollinearity. To overcome this problem, other methods such as LASSO and adaptive LASSO should be used. Both methods have the ability to overcome multicollinearity between independent variables, thereby reducing the risk of misestimation. Nevertheless, the LASSO and Adaptive LASSO methods have differences in selecting important variables, so it is necessary to compare which method is better in terms of identifying influential variables. Based on the MSE and R-square comparison values, it is concluded that the Adaptive LASSO method model is the best model with a lower MSE value and a higher R-square value of 93%. The variable selection results of the Adaptive LASSO model are population size, number of households, average number of household members, constant price GDP, confirmed cases of COVID-19, human development index, percentage of the poor population, university student participation rate, and open unemployment rate.

Impact of Storage Methods on Sweet Potato Spoilage in Plateau State Nigeria

Spoilage of sweet potato could be enormous when inadequate methods of storage are used. This research investigated impact of methods of storage on spoilage of sweet potato in three major markets of Plateau State Nigeria. Data for the study were obtained from sellers of sweet potato in Yan Doya market, New market and Farin Gada market of Plateau State through administration of questionnaires. Analysis of the data was carried out using descriptive statistics, Pearson Product Moment Correlation (PPMC), F-test statistics, t-test and Multiple Linear Regression (MLR) with backward elimination procedure. Results from descriptive statistics showed that majority of sweet potato sellers adopted use of sack for storage of their goods. The second method commonly used for storage of sweet potato in the study area was found to be root cellar method. Pair-wise application of PPMC indicated that spoilage of sweet potato was positively correlated with adopted methods of storage which suggested existence of relationship among the variables. Analysis using F-test statistics indicated that the relationship did not occur by chance between the variables hence t-test aided determination of which independent variables mainly influenced the dependent variable of the study. From the use of MLR with backward elimination procedure, spoilages from sacks and root cellars methods were the independent variables which mainly influenced the total spoilage of sweet potato in Plateau State Nigeria. Therefore, policy holders in Plateau State need to offer agricultural extension services that would provide modern storage facilities or educate sellers of sweet potato on best way to use both basket and root cellar methods which were their adopted methods commonly used for storage of sweet potato.

What Motivates Companies to Avoid Tax?

Main Purpose - This study looks into whether transfer pricing, corporate social responsibility, and leverage are factors that motivate companies to undertake tax avoidance of non-financial companies on the Indonesia Stock Exchange (IDX).Method - The sample consisted of 86 companies that were randomly selected by simple random sampling from 2017 to 2019. A total of 603 data observations consisting of the annual reports of 52 domestic companies and 34 multinational companies were analyzed by use of multiple linear regression with IBM SPSS 21 software.Main Findings – This study implies that the majority of companies consider the practice of tax avoidance to be unethical, the results show that the tier of tax-avoidance practices in companies that are listed on the IDX is very low. On the other hand, transfer pricing, corporate social responsibility, and leverage do not affect tax avoidance since those are not factors that cause companies to avoid taxes.Theory and Practical Implications - Most companies in Indonesia view tax avoidance as unethical even though it is legal. These findings are expected to contribute to regulators in setting tax-avoidance arrangements in Indonesia.Novelty - Compared to other studies, this study examines tax avoidance practices from an ethical perspective.

Focus on age at death in field epidemiology.

Age at death (AD) is an old metric recently re-evaluated for the study of longevity and mainly used in demography. Developed experience using AD in field epidemiology is summarized with cohorts followed-up for variable periods of time, frequently until extinction or close to extinction, a must to correctly adopt this metric. For practical purposes, a small number of examples is reported condensing previously published results to highlight various aspects of the problem. AD became the alternative of overall death rates when comparing cohorts reaching extinction or near extinction. AD was useful to characterize different causes of death in order to describe their natural history and possible etiology. With the use of multiple linear regression, a large number of possible determinants of AD were identified and some combinations of them resulted in large estimated differences in AD of 10years or more across individuals. AD is a powerful tool to study population samples followed-up until extinction or near extinction. It allows to compare the life-long experience of different populations, to compare the role of different causes of death and to study the determinants of AD that are conditioning longevity.

IMPACT OF DEBT AND EQUITY FINANCING ON FIRM PERFORMANCE DURING COVID-19 PANDEMIC IN SRI LANKA EMPIRICAL STUDY IN THE APPAREL AND TEXTILE INDUSTRY IN THE WESTERN PROVINCE

"One of Sri Lanka's top industries for earning foreign currency is clothing and textiles. However, the Covid19 pandemic adversely hampered the performance of the textile sector and seriously affected the company's economic operations. To continue in business Whatever the state of affairs are in the nation and the world, capitalizing is a must. There are primarily two forms of funding the business for that. Those are debt and equity financing. Accordingly,the study's primary objective is to investigate the effects of capital structure on business performance during the Covid-19 pandemic of small and medium scale enterprises in Sri Lanka’s apparel and textile industry. The study was a quantitative research study, employed a survey research strategy. A structured questionnaire was administered to collect primary data from responses and data was analyzed through statistical software SPSS 23.0. Sample size consists of 154 SMEs in apparel and textile industry in western province in Sri Lanka and sampling technique was stratified sampling technique. Employing Pearson's correlation analysis and multiple linear regression analysis, the conceptual framework was evaluated. The empirical findings from the use of multiple linear regression analysis showed that debt financing had a significant and positive influence on business performance whereas equity financing had a significant and negative influence on firm performance. The goodness of fitness of the overall model presented 55%. Out of nine hypotheses, three hypotheses were rejected through regression analysis. Borrowings, Other payables, Owner’s intangible assets were not significantly influenced on firm performance.Trade payables, contractual obligations, owners' cash on hand, and owners' physical assets all had a substantial impact on the operating activities of the garment and textile business during the Covid-19 outbreak.Based on the findings of the study, it was concluded that, SMEs in the apparel and textile industry employed both short-term debt and equity (retained earnings) to finance the operating activities of the firm during the Covid-19 pandemic and debt financing and equity financing significantly impacted on firm performance of the company.

Periodical evaluation of photovoltaic modules and diode parameter extraction method using multiple linear regression models

The stability and performance of photovoltaic (PV) modules can be assessed by outdoor testing where external conditions such as illumination and module temperature are measured at regular time intervals along with the jV-curve of the module. However, the fluctuation and seasonal variation of external conditions can make it difficult to trace changes such as degradation in PV-module properties (at e.g. standard test conditions). This contribution demonstrates the use of multiple linear regressions (MLR) to overcome these difficulties. The data gathered over large periods is condensed into a set of few predictors, that reproduce the jV parameters at infrequently encountered conditions that are required for comparison. Furthermore, the parameters of a physical device model are calculated directly from MLR-predictors, validating our procedure two-fold, by applying the MLR-method to simulated data, replicating the original input parameters, and comparing monthly parameter averages between the MLR-method and a known parameter extraction method.

The association between gender, stage and prognosis in bladder cancer patients undergoing radical cystectomy

Objective The incidence of bladder cancer is three times as high in men compared to women. Moreover, women are generally diagnosed with a more severe tumor stage and have poorer prognosis. This study aimed to examine the association between gender, stage, and prognosis among a subgroup of bladder cancer patients treated with radical cystectomy. Patients and methods A total of 460 patients (131 women, 329 men) with bladder cancer undergoing radical cystectomy at Aarhus University Hospital in 2015–2018 were retrospectively selected for this study and followed until 2021 at the latest. Correlations between gender, patient and tumor characteristics and oncological outcomes were analyzed by the Chi-squared test. By the use of multiple linear regression, we adjusted for age, comorbidity and the proportion of organ-confined and non-organ-confined disease at diagnosis. Results Female patients were found to be younger and less comorbid than male patients. A higher proportion of patients with muscle-invasive bladder cancer and non-organ-confined disease at the time of cystectomy was observed among female patients. Recurrence of cancer occurred 3.4 (0.1–6.7) months earlier in female patients, and they had a 47% higher cancer-specific mortality (RR = 1.47 (1.04–2.1)) compared to male patients. In the adjusted analysis, the association of an earlier recurrence in female patients remained. Conclusion This study verifies that gender disparities exist among bladder cancer patients, even after adjusting for age, comorbidity and for the proportion of organ-confined and non-organ-confined disease at cystectomy. Further investigations are required to investigate the etiology of this observed difference between sexes.